RAG Remains Core to Enterprise AI

RAG is becoming more important, not less, because better models increase the value of better inputs rather than eliminating the need for them. In practice, enterprise agents still need fresh web pages, internal documents, filings, and domain databases to answer questions that are time sensitive, company specific, or too detailed to fit inside model weights. The bottleneck is shifting from raw model intelligence to retrieval quality, source access, and validation.

• Cohere uses Tavily for web search in North even though Cohere builds its own models and retrieval stack. The reason is concrete. Search APIs that return URLs still require extra crawling and text extraction, while AI native search products package cleaner text for grounding, which makes downstream answers easier to build and verify.
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• The product split is becoming clearer. Tavily is closer to a search results and grounding API. Exa leans into semantic retrieval and domain streams like financial data. Parallel is pushing further into long running research workflows that can spend 10 to 15 minutes gathering and organizing evidence before writing an answer.
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• The durable risk is not that models stop needing external data. It is that retrieval vendors get squeezed into a commodity layer unless they control privileged data, workflow integration, or reliability at scale. Evidence from Ecosia and Cohere shows customers already design their systems to swap providers relatively quickly if quality and cost converge.
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Going forward, the winning retrieval layer is likely to look less like generic web search and more like a router into the right corpus for each task, internal files for enterprise questions, medical journals for clinical questions, filings for financial work, and live web results for current events. That keeps external information central, while raising the value of specialized connectors and domain specific indexes.